{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dfs2 import dfs2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Output parameters `x`, `y` format is `NumPy array`   \n",
    "`z` is dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.2    ,   0.2    ,   0.2    , ...,   0.2    ,   0.2    ,\n",
       "          0.2    ],\n",
       "       [  0.2    , -10.44383, -10.43223, ...,   0.2    ,   0.2    ,\n",
       "          0.2    ],\n",
       "       [  0.2    , -10.44424, -10.43262, ...,   0.2    ,   0.2    ,\n",
       "          0.2    ],\n",
       "       ...,\n",
       "       [  0.2    , -10.72805, -10.71805, ...,   0.2    ,   0.2    ,\n",
       "          0.2    ],\n",
       "       [  0.2    , -10.72472, -10.71453, ...,   0.2    ,   0.2    ,\n",
       "          0.2    ],\n",
       "       [  0.2    , -10.72139, -10.711  , ...,   0.2    ,   0.2    ,\n",
       "          0.2    ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'dfs2 FilePath'\n",
    "dfs2_obj = dfs2(path)\n",
    "x,y,z = dfs2_obj.read_dfs2()\n",
    "z['Bathymetry']['2002-12-31 23:59:59']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Example data is from MIKE_21 example file (Torsemide_Harbour).  \n",
    "`x`, `y` must do `numpy.meshgrid` ,because `x`, `y` is one dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x, y = np.meshgrid(x,y)\n",
    "z1 = z['Bathymetry']['2002-12-31 23:59:59']\n",
    "plt.figure()\n",
    "plt.contourf(x, y, z1,100)\n",
    "plt.colorbar()\n",
    "plt.axis('scaled')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
